ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · OCTOBER 2023
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
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GET: https://thatcarhitme.com/api/crash-data/reports/data/maryland/statewide/october-2023-report
Monthly Traffic Safety Analysis
991 CRASHES IN
MONTGOMERY, MD
OCTOBER 2023
In October 2023, Montgomery County recorded 991 total crashes, compared to 979 in October 2022, representing a 1.2% year-over-year increase. While the total number of crashes remained relatively stable, the number of fatalities saw a notable decrease, dropping from 7 in the prior period to 3 in the current period.
991
▲ 1.2%was 979
Total Crash Events
3
▼ -57.1%was 7
Persons Killed
320
▼ -4.8%was 336
Persons Injured
213
▲ 6.0%was 201
Hit-and-Run Crashes
Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 8 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Year-over-year, the total number of crashes in Montgomery County saw a slight increase of 1.2%, from 979 to 991. However, the severity of these crashes decreased, with total fatalities falling from 7 to 3 and total injuries declining from 336 to 320.
213
Hit-and-Run Crashes — October 2023
▲ 6.0% vs prior (201)
The number of hit-and-run incidents increased from 201 in October 2022 to 213 in October 2023, representing a 6.0% rise in the count of these crashes. The hit-and-run rate, which measures these incidents as a percentage of all crashes, also saw a slight upward trend, increasing from 20.5% to 21.5% year-over-year.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
0
Other Killed
40
Pedestrians Injured
12
Cyclists Injured
265
Motorists Injured
3
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted between the two periods. In October 2023, the peak day for crashes was Saturday with 150 incidents, and the peak hour was 3 PM with 82 crashes. This contrasts with October 2022, when the peak day was Monday (170 crashes) and the peak hour was 8 AM (74 crashes), indicating a shift from weekday morning commute times to weekend afternoon hours.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity decreased year-over-year. The fatal crash rate fell from 0.82 per 100 crashes in October 2022 to 0.40 in October 2023, with the count of fatal crashes dropping from 8 to 4. The proportion of crashes resulting in serious injuries also declined from 1.9% to 1.5%. Overall, crashes resulting in any form of reported injury (from possible to fatal) decreased as a share of all incidents from 29.8% to 27.9%.
Severity is per crash event (most severe injury). 4 fatal crash events resulted in 3 persons killed.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor in both periods was 'RAIN, SNOW, WET,' though its frequency decreased substantially. The count of crashes attributed to this factor dropped from 107 in October 2022 to 48 in October 2023, a 55.1% decrease in count. Similarly, the second-ranked factor, 'N/A, WET,' saw its crash count fall from 59 to 35. While the top factors remained the same, their overall contribution to crashes was lower in the current period.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The proportion of crashes occurring in adverse conditions was lower in October 2023 compared to the prior year. Crashes on wet road surfaces decreased from constituting 23.0% of all incidents in October 2022 to 10.6% in October 2023. Similarly, crashes during rain fell from representing 17.6% of the total to 9.3%. The distribution of crashes across different lighting conditions remained relatively stable between the two periods.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Road surface condition field
Vehicles & Demographics
The most common vehicle makes involved in crashes remained consistent year-over-year, with Toyota, Honda, and Ford being the top three in both periods. After consolidating variations in make names, Toyota-branded vehicles were involved in 336 crashes in October 2023, up from 309 in the prior year, while Honda involvement decreased from 260 to 244. The distribution of vehicle types was also stable, with 'Passenger Car' and '(Sport) Utility Vehicle' being the most frequent types in both October 2023 and October 2022.
Top Vehicle Makes (1,749 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-10-01 to 2023-10-31 · Vehicle unit records
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Montgomery County Crash Reporting (ACRS) (https://data.montgomerycountymd.gov/d/bhju-22kf), accessed programmatically via the Socrata Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.
Data Retrieval
- Access method: Socrata Open Data API (SoQL queries)
- Dataset URL: https://data.montgomerycountymd.gov/d/bhju-22kf
- Data format: Structured JSON via REST API
- Record types queried: Crash events, person records, and vehicle unit records
- Date filter applied: 2023-10-01 through 2023-10-31
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2023-10-01 through 2023-10-31 (31 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 991
- Total persons involved: 1,814
- Total vehicles involved: 1,749
Analytical Methodology
- Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
- Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
- Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
- Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
- Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
- Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
- AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.
Limitations & Disclaimers
- Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
- Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
- Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
- AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
- Percentages are calculated from reported data and are subject to rounding.
Non-Affiliation Disclosure
This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.
Data License
The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.
Corrections & Feedback
If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.
Suggested Citation
ThatCarHitMe.com (Injuria.ai). "montgomery, MD Crash Intelligence Report: October 2023." Published September 9, 2026. Reporting period: 2023-10-01 to 2023-10-31. Data source: Montgomery County Crash Reporting (ACRS), Socrata Open Data. Dataset: https://data.montgomerycountymd.gov/d/bhju-22kf. Available at: https://thatcarhitme.com/crash-data/maryland/statewide/october-2023-report
About the Publisher
ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.
Questions about this report's data or methodology: data@injuria.ai
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Montgomery County Crash Reporting (ACRS) · Socrata
Period: 2023-10-01 – 2023-10-31
Generated: September 9, 2026 · All rights reserved